Open Collections

Comparison Between Sentinel-2 and Landsat in Mapping Mangroves in The Gambia Using the Google Earth Engine Mangrove Mapping Methodology (GEM v2)

Mituro, Farida

Mangroves play a vital role in coastal environments by supporting biodiversity, protecting shorelines, and storing carbon. However, these ecosystems are under growing pressure from land use change, development, and rising sea levels. Monitoring changes in mangrove cover is essential for guiding conservation and management efforts, but on the ground, methods are often time-consuming, expensive, and difficult to carry out across large or remote areas. This is where remote sensing offers a practical solution. By using satellite imagery, it becomes possible to track changes in mangrove extent over time, detect patterns of loss and regrowth, and support decision-making at both local and national levels. This study compared the effectiveness of Sentinel-2 and Landsat satellite data in mapping mangrove dynamics in The Gambia, using the Google Earth Engine Mangrove Mapping Methodology version 2 (GEM v2) from Blue Venture, a marine and conservation organization. A Random Forest classifier was applied to classify land cover types, and the accuracy of each dataset was evaluated. Sentinel-2 was better at detecting small, detailed changes, including narrow strips of loss and early signs of regrowth. Landsat, while less detailed, provided more stable classifications across broader areas. Sentinel-2 also showed greater variation in spectral data, which led to more classification errors in some mangrove classes, while Landsat maintained higher overall accuracy. The results show that both datasets have strengths, Sentinel-2 is well-suited for close-up monitoring of small-scale changes, while Landsat is reliable for long-term, large-scale analysis. A combined approach using both sensors may offer the complete overview of mangrove change. Future studies should consider using field data from the same area to improve classification accuracy and support more effective monitoring and protection of mangrove ecosystems.

Dataset

2025-04-09

CC-BY 4.0

https://doi.org/10.5683/SP3/UNZTIH

http://creativecommons.org/licenses/by/4.0